It is clear that differences exist between the molecular mechanisms in HPV-induced squamous cell carcinomas (SCCs) and those linked to tobacco carcinogens. Currently, treatment of head and neck SCC (HNSCC) is not based on HPV status. The incidence of tonsil and tongue cancers have been increasing annually and many contain high risk HPV. It is important to understand differing factors of carcinogenesis in HPV(+) and HPV(-) HNSCC to develop personalized treatment approaches. To do this requires more knowledge of the molecular mechanisms that drive tumor behavior and response to therapy in HPV(+) vs HPV(-) SCCs. Our preliminary data indicate that there are striking epigenetic differences between HPV+ and HPV- tumors, but to appreciate these differences, they must be considered in light of gene expression and other somatic changes. We propose to use gene expression, DNA methylation and histone modifications, and copy number changes to identify molecular mechanisms that define and differentiate HPV-induced from carcinogen-induced HNSCC. The overall objective of this proposal is to understand the differences in the aberrant molecular pathways leading to carcinogenesis in HPV(+) and HPV(-) HNSCCs taking into account smoking and additional epidemiological factors. Our central hypothesis is that by using advanced, integrative bioinformatics methods on the genomic and epigenomic profiles of HPV+ and HPV- tumor cells, we will be able to subdivide HPV+ and HPV- tumors into high and low risk subsets. Our long term goal is to accurately predict and apply the most appropriate treatment regimes for individual HPV+ and HPV- HNSCCs based on smoking, molecular factors and new targets identified in this study. In the first aim, whole-genome analyses will be performed on a well-characterized panel of HPV+ and HPV- oropharyngeal cell lines and primary oral/oropharyngeal (OPSCC) tumors from HPV+ smokers, HPV+ non-smokers, and HPV- ever smokers, and relevant normal cells to define and distinguish aberrant molecular pathways for each etiology. Aim 2 will integrate and characterize genomic, epigenomic and corresponding gene expression changes to prioritize results based on clinical relevancy by developing, validating, and applying integrative methods for the analysis of multifaceted deep sequencing data. Aim 3 will identify and validate top prioritized findings in a larger sample of primary tumor samples. This will confirm clinically important biomarkers and identify aberrant changes associated with etiology, recurrence, or survival in tumor cells from clinical specimens. Our tiered approach from high-throughput technologies to validation in a patient population together with innovative bioinformatics approaches will pave the way to understanding and exploiting somatic differences for optimal therapeutic application. Collectively, our proposed studies will bring us closer to personalized treatment regimes for OPSCCs, as well as provide valuable, accessible tools to the research community for integrative analysis and interpretation of deep sequencing data.